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TensorFlow Machine Learning Cookbook

You're reading from   TensorFlow Machine Learning Cookbook Over 60 recipes to build intelligent machine learning systems with the power of Python

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Product type Paperback
Published in Aug 2018
Publisher Packt
ISBN-13 9781789131680
Length 422 pages
Edition 2nd Edition
Languages
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Authors (2):
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Sujit Pal Sujit Pal
Author Profile Icon Sujit Pal
Sujit Pal
Nick McClure Nick McClure
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Nick McClure
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Toc

Table of Contents (13) Chapters Close

Preface 1. Getting Started with TensorFlow FREE CHAPTER 2. The TensorFlow Way 3. Linear Regression 4. Support Vector Machines 5. Nearest-Neighbor Methods 6. Neural Networks 7. Natural Language Processing 8. Convolutional Neural Networks 9. Recurrent Neural Networks 10. Taking TensorFlow to Production 11. More with TensorFlow 12. Other Books You May Enjoy

Introduction

So far, we have covered how to train and evaluate various models in TensorFlow. So, in this chapter, we will show you how to write code that is ready for production use. There are various definitions of production-ready code, but for us, production code will be defined as code that has unit tests, separates the training and evaluation code, and efficiently saves, and loads the various required parts of the data pipeline and graph session.

The Python scripts provided in this chapter should be run from the command line. This allows tests to be run, and device placements to be logged to the screen.
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